Global Artificial Bee Colony - Semantic Scholar
58 International Journal of Applied Evolutionary Computation, 4(3), 58-74, July-September 2013
Global Artificial Bee ColonyLevenberq-Marquardt (GABCLM) Algorithm for Classification Habib Shah, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Rozaida Ghazali, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Nazri Mohd Nawi, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Mustafa Mat Deris, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Tutut Herawan, Department of Mathematics Education, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
ABSTRACT The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task. Keywords:
Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Global Hybrid Ant Bee Colony, Hybrid Ant Bee Colony (HABC) Algorithm, Swarm Intelligence
DOI: 10.4018/jaec.2013070106 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
International Journal of Applied Evolutionary Computation, 4(3), 58-74, July-September 2013 59
INTRODUCTION Artificial Neural Networks (ANNs) are the most novel and powerful artificial tool suitable for solving combinatorial problems such as prediction, forecasting and classification (Daqi & Yan, 2005; de A. Araújo, 2011; Ghazali, Jaafar Hussain, Mohd Nawi, & Mohamad, 2009). NNs are being used extensively for solving universal problems intelligently like continuous, discrete, telecommunications fraud detection and clustering (Charalampidis & Muldrey, 2009; Hilas & Mastorocostas, 2008; Mielniczuk & Tyrcha, 1993). Ns are being applied for different optimization and mathematical problems such as classification, object and image recognition, signal processing, temperature and weather forecasting and bankruptcy (Aussem, Murtagh, & Sarazin, 1994; Chaudhuri & Bhattacharya, 2000; Chen, Duan, Cai, & Liu, 2011; Uncini, 2003). There are several techniques used for NNs favourable performance for training ANN such as evolutionary algorithms (EA), Multi-objective hybrid evolutionary algorithms (Qasem, Shamsuddin, & Zain, 2012; TallónBallesteros & Hervás-Martínez, 2011), Genetic algorithms (GA) (Blanco, Delgado, & Pegalajar, 2001; García-Pedrajas, Ortiz-Boyer, & HervásMartínez, 2006; Geretti & Abramo, 2011), Particle swarm optimization (PSO) (Al-Shareef & Abbod, 2010; Hong-Bo, Yi-Yuan, Jun, & Ye, 2004; Hongwen & Rui, 2006), deferential evolution (DE) (Slowik & Bialko, 2008; Subudhi & Jena, 2011), an colony optimization (ACO) (Ashena & Moghadasi, 2011; Blum & Socha, 2005), BP d improved BP algorithm (Nawi, Ransing, & Ransing, 2006; Yan, Zhongjun, & Jiayu, 2010). These techniques are used for initialization of optimum weights, parameters, activation function, and selection of a proper network structure. The main task of BP algorithm is to update the network weights for minimizing output error using BP processing because the accuracy of any approximation depends upon the selection of proper weights for the neural networks (NNs). It has high success rate in solving many complex
problems, but it still has some drawbacks, especially when setting parameter values like initial values of connection weights, value for learning rate, and momentum. If the network topology is not carefully selected, the NNs algorithm can get trapped in local minima, or it might lead to slow convergence or even network failure. In order to overcome the disadvantages of standard BP, much global optimization population-based technique[20], GA (Geretti & Abramo, 2011), improved BP (Nawi, et al., 2006), DE (Slowik & Bialko, 2008), BP-ant colony(Chengzhi, Yifan, Lichao, & Yang, 2008), and PSO (Hongwen & Rui, 2006). Artificial Bee Colony (ABC) (D. Karaboga & Akay, 2007), he Hybrid Ant Bee Colony (HABC) (H. SHah, R. Ghazali, N. M. Nawi, & M. M. Deris, 2012b), Improved Artificial Bee Colony (IABC) (H. Shah & Ghazali, 2011),G-HABC (H. Shah, R. Ghazali, N. M. Nawi, & M. M. Deris, 2012b,;Hybrid Global Artificial Bee Colony Algorithm (Rozaida et al.,2013) and the Global Hybrid Ant Bee Colony (HABC) algorithm (Habib SHah, et al., 2012b), ar population-based algorithms that can provide the best possible solutions for different mathematical problems by using inspiration techniques from nature. A common feature of population-based algorithms is that the population consisting of feasible solutions to the difficulty is customized by applying some agents on the solutions depending on tupon information of their robustness. Therefore, the population is encouraged towards improved solution areas of the solution space. Population-based optimization algorithms are categories into two sections namely evolutionary algorithm (EA) (Xinyan & Jianguo, 2011), and I-based algorithm (Aydin, Wu, & Liang, 2010) . In EA, the major plan underlying this combination is to take the weight matrices of the ANNs as individuals, to change the weights by some operations such as crossover and mutation, and to use the error produced by the NNs as the fitness measure that guides selection (Yan-fei & Xiong-min, 2010). In S based algorithm, ABC has the advantage of global optimization and easy recognition. It has been successfully used
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Global Artificial Bee ColonyLevenberq-Marquardt (GABCLM) Algorithm for Classification Habib Shah, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Rozaida Ghazali, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Nazri Mohd Nawi, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Mustafa Mat Deris, Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia Tutut Herawan, Department of Mathematics Education, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
ABSTRACT The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task. Keywords:
Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Global Hybrid Ant Bee Colony, Hybrid Ant Bee Colony (HABC) Algorithm, Swarm Intelligence
DOI: 10.4018/jaec.2013070106 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
International Journal of Applied Evolutionary Computation, 4(3), 58-74, July-September 2013 59
INTRODUCTION Artificial Neural Networks (ANNs) are the most novel and powerful artificial tool suitable for solving combinatorial problems such as prediction, forecasting and classification (Daqi & Yan, 2005; de A. Araújo, 2011; Ghazali, Jaafar Hussain, Mohd Nawi, & Mohamad, 2009). NNs are being used extensively for solving universal problems intelligently like continuous, discrete, telecommunications fraud detection and clustering (Charalampidis & Muldrey, 2009; Hilas & Mastorocostas, 2008; Mielniczuk & Tyrcha, 1993). Ns are being applied for different optimization and mathematical problems such as classification, object and image recognition, signal processing, temperature and weather forecasting and bankruptcy (Aussem, Murtagh, & Sarazin, 1994; Chaudhuri & Bhattacharya, 2000; Chen, Duan, Cai, & Liu, 2011; Uncini, 2003). There are several techniques used for NNs favourable performance for training ANN such as evolutionary algorithms (EA), Multi-objective hybrid evolutionary algorithms (Qasem, Shamsuddin, & Zain, 2012; TallónBallesteros & Hervás-Martínez, 2011), Genetic algorithms (GA) (Blanco, Delgado, & Pegalajar, 2001; García-Pedrajas, Ortiz-Boyer, & HervásMartínez, 2006; Geretti & Abramo, 2011), Particle swarm optimization (PSO) (Al-Shareef & Abbod, 2010; Hong-Bo, Yi-Yuan, Jun, & Ye, 2004; Hongwen & Rui, 2006), deferential evolution (DE) (Slowik & Bialko, 2008; Subudhi & Jena, 2011), an colony optimization (ACO) (Ashena & Moghadasi, 2011; Blum & Socha, 2005), BP d improved BP algorithm (Nawi, Ransing, & Ransing, 2006; Yan, Zhongjun, & Jiayu, 2010). These techniques are used for initialization of optimum weights, parameters, activation function, and selection of a proper network structure. The main task of BP algorithm is to update the network weights for minimizing output error using BP processing because the accuracy of any approximation depends upon the selection of proper weights for the neural networks (NNs). It has high success rate in solving many complex
problems, but it still has some drawbacks, especially when setting parameter values like initial values of connection weights, value for learning rate, and momentum. If the network topology is not carefully selected, the NNs algorithm can get trapped in local minima, or it might lead to slow convergence or even network failure. In order to overcome the disadvantages of standard BP, much global optimization population-based technique[20], GA (Geretti & Abramo, 2011), improved BP (Nawi, et al., 2006), DE (Slowik & Bialko, 2008), BP-ant colony(Chengzhi, Yifan, Lichao, & Yang, 2008), and PSO (Hongwen & Rui, 2006). Artificial Bee Colony (ABC) (D. Karaboga & Akay, 2007), he Hybrid Ant Bee Colony (HABC) (H. SHah, R. Ghazali, N. M. Nawi, & M. M. Deris, 2012b), Improved Artificial Bee Colony (IABC) (H. Shah & Ghazali, 2011),G-HABC (H. Shah, R. Ghazali, N. M. Nawi, & M. M. Deris, 2012b,;Hybrid Global Artificial Bee Colony Algorithm (Rozaida et al.,2013) and the Global Hybrid Ant Bee Colony (HABC) algorithm (Habib SHah, et al., 2012b), ar population-based algorithms that can provide the best possible solutions for different mathematical problems by using inspiration techniques from nature. A common feature of population-based algorithms is that the population consisting of feasible solutions to the difficulty is customized by applying some agents on the solutions depending on tupon information of their robustness. Therefore, the population is encouraged towards improved solution areas of the solution space. Population-based optimization algorithms are categories into two sections namely evolutionary algorithm (EA) (Xinyan & Jianguo, 2011), and I-based algorithm (Aydin, Wu, & Liang, 2010) . In EA, the major plan underlying this combination is to take the weight matrices of the ANNs as individuals, to change the weights by some operations such as crossover and mutation, and to use the error produced by the NNs as the fitness measure that guides selection (Yan-fei & Xiong-min, 2010). In S based algorithm, ABC has the advantage of global optimization and easy recognition. It has been successfully used
Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
15 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/global-artificial-bee-colonylevenberq-marquardt-gabc-lm-algorithm-forclassification/95959?camid=4v1
This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2
Related Content Reliable Face Recognition Using Artificial Neural Network Shaimaa A. El-said (2013). International Journal of System Dynamics Applications (pp. 14-42).
www.igi-global.com/article/reliable-face-recognition-using-artificial-neuralnetwork/78749?camid=4v1a Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments Joe Hoffert, Aniruddha Gokhale and Douglas C. Schmidt (2013). Innovations and Approaches for Resilient and Adaptive Systems (pp. 172-195).
www.igi-global.com/chapter/timely-autonomic-adaptation-publishsubscribe/68950?camid=4v1a Mobility Management in Publish/Subscribe Middleware Fatma Abdennadher and Maher Ben Jemaa (2013). International Journal of Adaptive, Resilient and Autonomic Systems (pp. 101-112).
www.igi-global.com/article/mobility-management-publish-subscribemiddleware/75551?camid=4v1a
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www.igi-global.com/article/accounting-lean-implementation-governmententerprise/76347?camid=4v1a
